Activation mechanism of the human Smoothened receptor

  1. Prateek D. Bansal
  2. Soumajit Dutta
  3. Diwakar Shukla

  • Curated by Biophysics Colab

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    Evaluation statement (22 August 2023)

    Bansal et al. present an atomistic view of the transition cascade of the class F GPCR Smoothened (Smo). The extensive long-range molecular dynamics simulations together with stochastic modelling provide theoretical insight into Smo activation and how this is modulated by different ligands. The work identifies testable hypotheses for functional studies of Smo and other class F GPCRs. Future simulations of regions beyond the seven-transmembrane bundle, particularly the cysteine-rich domain, will afford a more complete understanding of receptor activation.

    Biophysics Colab considers this to be a convincing computational study and recommends it to scientists interested in the conformational dynamics of class F GPCRs.

    (This evaluation by Biophysics Colab refers to version 2 of this preprint, which has been revised in response to peer review of version 1.)

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Abstract

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  1. Biophysics Colab

    Evaluation statement (22 August 2023)

    Bansal et al. present an atomistic view of the transition cascade of the class F GPCR Smoothened (Smo). The extensive long-range molecular dynamics simulations together with stochastic modelling provide theoretical insight into Smo activation and how this is modulated by different ligands. The work identifies testable hypotheses for functional studies of Smo and other class F GPCRs. Future simulations of regions beyond the seven-transmembrane bundle, particularly the cysteine-rich domain, will afford a more complete understanding of receptor activation.

    Biophysics Colab considers this to be a convincing computational study and recommends it to scientists interested in the conformational dynamics of class F GPCRs.

    (This evaluation by Biophysics Colab refers to version 2 of this preprint, which has been revised in response to peer review of version 1.)

  2. Version published to 10.1016/j.bpj.2023.03.007
  3. Biophysics Colab

    Authors' response (24 January 2023)

    GENERAL ASSESSMENT

    The rationale behind the study:

    While the activation mechanisms of Class A and Class B GPCRs have been extensively studied. little emphasis has been placed on the study of the dynamics of class F receptors such as Smoothened (SMO). Hence it is still elusive which motifs may take part in the transition from inactive to active conformations. Understanding the underpinnings of receptor activation in terms of residue networks. and their modulation by allosteric modulators. could help rational drug design. such as for novel SMO antagonists for cancer treatment.

    Key findings and major conclusions:

    Bansal, Dutta and Shukla perform extensive molecular dynamics (MD) simulations in conjunction with Markov State Model theory for a range of conformational starting points (apo. agonist. and antagonist bound states) to elucidate a dynamic overview of SMO activation. This has mostly remained elusive despite the availability of inactive and active-state SMO structures. They reveal conserved motifs important for activation of class F receptors, which are distinct from other known activation motifs. including from class A or B GPCRs. The long-range MD simulations together with free energy calculations also identified three additional intermediate states between inactive and fully active SMO. Furthermore. they provide structural support for how the specific function of antagonists and agonists modulate the cholesterol tunnel and thereby modulate SMO's activity. Finally. the authors present the dynamic allosteric pathway at atomistic resolution between the extracellular and intracellular side during activation and upon ligand modulation. Taken together, the authors provide a more detailed understanding of Class F GPCRs that could serve as the foundation for specific experimental validation studies.

    The perceived strengths and weaknesses:

    The new perspectives on the conformational changes during activation of SMO are based on well-described MD simulations. The Class F activation mechanism is not well understood: hence the authors· conclusions advance the field by identifying states that are distinct from class A GPCRs and how cholesterol can modulate SMO's activity, resulting in a map of allosteric pathways for this receptor type. However, the stated uniqueness or proposed Class F-specific observations would be more definitive with additional analyses.

    We sincerely thank the reviewers for providing insightful and constructive comments on our work. We believe that the reviewers' suggestions have improved the quality and clarity of the manuscript. The consolidated report was extremely helpful.

    Based on the reviews, we extensively revised the main text and figures to accommodate the suggestions and required additional calculations.

    RECOMMENDATIONS

    Revisions essential for endorsement:

    1. The title seems too comprehensive for the present study. Please consider a title that more accurately summarizes the specific work in this manuscript.

    We thank the reviewer for this suggestion. However, unfortunately the manuscript is under review and the title has already been submitted to the journal.

    1. [Methods - Pre-Production MD, page 20]: The authors chose a more complex membrane composition to mimic physiological cerebellar membranes that requires additional attention during equilibration. If this has not been undertaken (no note in the method section). we do recommend carefully investigating the lipid distributions/clustering. including unusual curvature, that might influence the receptors behaviour throughout the simulations, in particular if modulations by ligands are interpreted.

    We thank the reviewer for this concern. The equilibration performed was performed for 40ns, and we found no unusual curvature changes on visual inspection. Regarding the lipid distribution, we have plotted the cholesterol distributions for the ensembles and observe a conformation-dependence on lipid organization in the membrane. We plotted the x-y distribution of cholesterol in both the leaflets in the membrane and do observe that cholesterol shows a concentrated density between TM2 and TM3 in inactive SMO. This concentration of cholesterol, however, is not seen in active SMO (both leaflets) and inactive SMO (lower leaflet). This further provides evidence that SMO indeed shows a propensity to cholesterol in the inactive state, given that cholesterol is the endogenous activating molecule for SMO. This additional discussion has been added to the manuscript for further clarification.

    Added text: Results and Discussion, SMO's Activation is linked to opening of a hydrophobic tunnel.

    "Interestingly, we observe a conformational dependence of the lipid organization in the membrane - Inactive SMO surrounds itself with a cholesterol in the upper leaflet, as opposed to other cases (Fig. S20). This suggests that cholesterol shows a propensity to accumulate outside inactive SMO to possibly transport itself in the hydrophobic tunnel, leading to SMO activation."

    1. [Methods]: The authors should discuss convergence of the simulated clusters and energy landscape prior to conducting Markov State Modeling.

    We thank the reviewer for this concern. The convergence of the energy landscape and the simulated clusters can be corroborated by the presence of continuous density between the inactive and the active states along time-independent component 1 in the tIC landscape (Fig. S9). The tIC plot shows the kinetically slowest processes, hence the simulated data has sampled the full conformational landscape associated with SMO activation. We have added additional text in the manuscript for further clarification.

    Added text: Results and Discussion, SMO activation involves a conserved molecular switch.

    "The convergence of the data, clusters and hence the free energies derived from it, were confirmed by the presence of a continuous density of data along tIC 1 (Fig. S9A). This shows that the simulations have indeed sampled the conformational landscape necessary to probe the activation pathway of SMO."

    1. DRY motif:
    • Please clarify the statements about generalisation in Class F in light of the missing outward kinking. As this praline is present in other Class F receptors. it suggests that this activation feature is likely unique to SMO. Also this molecular switch should be compared to e.g. Rhodopsin. Which also signals through Gi (see e.g. Hofmann et al. doi:10.1016/j.tibs.2009 .07.005)

    We thank the reviewers for this additional clarification. Text has been added to the manuscript to reflect the changes suggested.

    Modified Text: Results, SMO activation involves a conserved molecular switch:

    "This particular feature is hence unique to the activation mechanism of SMO."

    • The authors should consider additional evidence or be more careful in their statement that the conserved motif (W3.50-G5.39-M6.30) acts as a microswitch in SMO signal transduction. The authors claimed that it is analogous to the DRY motif in class A GPCRs. However, the DRY motif has been shown to be involved in both inactive and active states. validated by experimental data. The R3.50 in DRY motif forms an ionic lock in the inactive state and this ionic interaction is broken during receptor activation. It is unclear what interactions are formed between the W-G-M motif in SMO.

    We agree, and further experimental validation is required for establishing a role for the WGM motif. We posit that this motif works as a microswitch, mediated by hydrophobic interactions. The text in the manuscript has been modified to clarify this.

    • Similar to W-G-M, the D-R-E motif has also been claimed as an important site for signal transduction. We recommend caution in this conclusion. The authors mentioned there is an H-bond interaction between D473 and E518. Is there a water molecule between these two residues? The two residues have very low pKa for the carboxylate group and probably are devoid of hydrogens in physiological conditions. Figure 2C should include the R400.

    The pKa of the E518 has been evaluated using the H++ server, and E518 was found to be protonated under physiological conditions. Hence the presence of a H-bond between D473 and E518 is plausible. We have added additional clarification in the methods section to clarify this point. R400 has been included in the figure.

    1. The statement "SAG acts as an agonist by allosterically expanding the tunnel at the cholesterol interaction site" (line 252) may be incorrect according to at least two lines of evidence: 1) elongation of the 4-aminomethyl group of SAG converts it to an antagonist; 2) SMO variants containing mutations at the cholesterol binding site don't respond to SAG as described in Deshpande et al (Nature 571. 284-288 (2019)). The agonist activity of SAG is most likely due to blocking cholesterol in the 7-T Ms. The authors may want to change the statement and conclusion or provide strong evidence to support it.

    We agree, and the statement "SAG acts as an agonist by allosterically expanding the tunnel at the cholesterol interaction site" supports both the conclusions mentioned above. The following justification is provided:

    • SAG's binding position lies right outside the proposed tunnel for cholesterol transport, making it a viable candidate as an agonist for SMO. The leading hypothesis for cholesterol transport-like endogenous activity for SMO states that the cholesterol enters the protein through the membrane, considering that previous structures (Byrne et al., Nature, 2016) have posited that a cholesterol must exit the plasma membrane in order to activate SMO. The antagonist-like activity of the addition of the 4-aminomethyl moiety may be alternatively explained as follows: The addition of the 4-aminomethyl moiety probably precludes the opening of the tunnel in the membrane by blocking the allosteric communication, and hence precludes cholesterol transport to the core TM from the membrane. However, further experimental validation is required for probing the exact role of the 4-aminomethyl moiety in SAG. Additionally, recent studies have provided further evidence for the CRD site being the orthosteric binding site for endogenous activation mechanism of SMO (Kinnebrew et al., eLife, 2021; Kinnebrew et al., Sci. Adv., 2022). For cholesterol to reach the orthosteric binding site, the tunnel must open between the membrane and the TM domain. We find through our studies that SAG-bound SMO shows such an opening, specifically between TM2 and TM3, which has been shown to be a binding site for cholesterol. (Hedger et al., Structure, 2019). Additionally, this opening is also present in the upper leaflet, which has been shown to be the leaflet through which PTCH controls SMO's activity. (Kinnebrew et al., eLife, 2021).

    • Regarding the mutations in the cholesterol binding site which make it insensitive to SAG binding, these observations can also be explained based on the requirement of cholesterol to transport to the orthosteric binding site (the CRD site) for endogenous activation. Further extensive experimental evidence is hence required to completely understand the endogenous activation mechanism of SMO.

    We sincerely thank the reviewer for this question.

    Additional suggestions for the authors to consider:

    1. [Fig S.19]: Validity of the MSM on 5 macrostates via the Chapman-Kolmogorov test: the predictions and estimates look identical. Please add a 95% confidence interval and provide scripts used for the calculation and plotting.

    We thank the reviewer for this suggestion. Error has been estimated using bootstrapping for the 5-macrostate MSM. Bootstrapping was performed on 200 sets of randomly selected 80% trajectories. The errors have been added to the Chapman-Kolmogorov test and the scripts have been uploaded to the github link.

    1. Did the authors try to simulate SMO with cholesterol bound to the cysteine rich domain (CRD)? The reorientation of CRD revealed by xSMO crystal structures is controversial in the field because this movement may be a result of crystal packing. It will be very interesting to test whether CRD can undergo this reorientation after cholesterol binding by MD simulation.

    Unfortunately, the simulations of cholesterol bound SMO are outside the scope of this manuscript. We however do thank the reviewer for their suggestion.

    1. [Methods, page 19 line 332]: It is not entirely clear what preparation was done to the SANTl-SMO structure. Please rephrase the sentences to ensure reproducibility.

    We thank the reviewer for this suggestion. We have rephrased the preparation steps for SANT1-SMO structure for further clarity.

    Edited text: Methods, MD Simulations, Simulation Setup

    "For SANT1-SMO, owing to the lack of the CRD in the SANT1-SMO complex (PDB ID: 4N4W(35)), we sought to use the inactive orientation of 5L7D (inactive SMO, CRD present) instead. The SANT1-bound crystal structure (4N4W) was aligned to inac-SMO 5L7D (to maintain the same binding pose for SANT1), and the 5L7D-SANT1 starting point was used for simulations."

    1. [Methods]: General structure preparation: Where the structures solvated prior insertion into the membrane to avoid collapsing of cavities?

    The protein structures were prepared using the CHARMM-GUI webserver, which uses an optimized library of lipid conformations to prevent formation of cavities in the membrane. Hence the need to solvate the structures a priori was obviated.

    1. [Methods - MD, page 20]: The authors do not mention the used force-field. Please add.

    The force-field used for all simulations was CHARMM36. The force field has been mentioned in the Methods section.

    1. [Figure S6]: For clarification and comparison (in context of uniqueness). Please show the changes in the residues involved in the microswitch for b2AR (6.30, 5.58. 5.66 - also shown for Rhodopsin and others).

    The changes for the appropriate microswitch have already been shown in the figure.

    1. [Figure 3]: the authors compared the CRD-TMD junction between inactive and active states. How is the conformation of these residues compared to the determined structures of SMO?

    The conformation of the residues is the same as the determined structures of SMO. We have added an additional figure to compare the structure with the inactive-active structures of SMO.

    1. [Results]: It is very interesting that three intermediate states have been determined between inactive and active states. It is unclear how these states (11., 12, 13) are defined, besides the energy barrier. Are there any signature residues or motifs that can represent each intermediate state?

    The states I1-3 have been defined purely on the basis of free energy differences. Attempts to uniquely identify motifs present in each state I1-3 have not been fruitful, as these motifs are present at a very dynamic domain of the protein (CRD). Hence, attempts to identify unique motifs representing each structure have not been fruitful.

    1. Cholesterol interactions, distributions and modulations could give valuable insight into their influence on the activation mechanism. As cholesterol is present in the simulations, this data could be easily screened for cholesterol receptor interactions throughout the activation pathway.

    The response to this question has already been made a part of the response to 'Revisions essential for endorsement' point 2.

    1. Experimental structures have already revealed the conformational changes between inactive and active SMO, in particular, the shift of TM6 and the movement of W535. This should be clarified in the text and interpreted in light of the new results.

    We thank the reviewer for this comment. The text has been modified in the manuscript to reflect the suggested changes.

    Modified Text: Introduction:

    "Mutagenesis studies have outlined the presence of an intracellular W7.55𝑓 -R6.32𝑓𝜋-cation lock(40) in Class F that is broken on activation (Fig. 1A)"

    Modified Text: Results, SMO activation involves a conserved molecular switch:

    "M4496.30𝑓's outward movement is a proxy for the outward movement of TM6 a process associated with canonical GPCR activation(33, 38)"

    1. While the authors calculated the mean first passage times during the apo simulation, they did not correlate this to the presence and absence of agonists. This could give further insight into how those modulators are influencing the activation pathway.

    The mean first passage times could not be calculated in the presence of agonists, as the different intermediate states observed during apo-simulation (I1-3) were not observed during the agonist-bound simulations. Hence, we can say that the presence of the agonist locks the protein in an active state, unable to be explored using unbiased MD-simulations.

    1. [Methods]: The used analysis scripts could be deposited/made available (e.g. how the Chapman-Kolmogorov test was implemented).

    We appreciate the reviewer's request for the scripts. The scripts have been uploaded to github.

    1. The study would have a greater influence on the field by further investigations on the agreement between simulations and experiments.

    We agree. Experimental validation will play a key role in further uncovering and verifying the claims made in this manuscript. As a guide to experimentalists, wherever possible, we have added suggested mutations for further verifying the computational study.

    (This is a response to peer review conducted by Biophysics Colab on version 1 of this preprint.)

  4. Version published to 10.1101/2022.06.07.493647v2 on bioRxiv
  5. Biophysics Colab

    Consolidated peer review report (8 September 2022)

    GENERAL ASSESSMENT

    The rationale behind the study:

    While the activation mechanisms of Class A and Class B GPCRs have been extensively studied, little emphasis has been placed on the study of the dynamics of class F receptors such as Smoothened (SMO). Hence it is still elusive which motifs may take part in the transition from inactive to active conformations. Understanding the underpinnings of receptor activation in terms of residue networks, and their modulation by allosteric modulators, could help rational drug design, such as for novel SMO antagonists for cancer treatment.

    Key findings and major conclusions:

    Bansal, Dutta and Shukla perform extensive molecular dynamics (MD) simulations in conjunction with Markov State Model theory for a range of conformational starting points (apo, agonist, and antagonist bound states) to elucidate a dynamic overview of SMO activation. This has mostly remained elusive despite the availability of inactive and active-state SMO structures. They reveal conserved motifs important for activation of class F receptors, which are distinct from other known activation motifs, including from class A or B GPCRs. The long-range MD simulations together with free energy calculations also identified three additional intermediate states between inactive and fully active SMO. Furthermore, they provide structural support for how the specific function of antagonists and agonists modulate the cholesterol tunnel and thereby modulate SMO’s activity. Finally, the authors present the dynamic allosteric pathway at atomistic resolution between the extracellular and intracellular side during activation and upon ligand modulation. Taken together, the authors provide a more detailed understanding of Class F GPCRs that could serve as the foundation for specific experimental validation studies.

    The perceived strengths and weaknesses:

    The new perspectives on the conformational changes during activation of SMO are based on well-described MD simulations. The Class F activation mechanism is not well understood; hence the authors’ conclusions advance the field by identifying states that are distinct from class A GPCRs and how cholesterol can modulate SMO’s activity, resulting in a map of allosteric pathways for this receptor type. However, the stated uniqueness or proposed Class F-specific observations would be more definitive with additional analyses.

    RECOMMENDATIONS

    Revisions essential for endorsement:

    1. The title seems too comprehensive for the present study. Please consider a title that more accurately summarizes the specific work in this manuscript.

    2. [Methods - Pre-Production MD, page 20]: The authors chose a more complex membrane composition to mimic physiological cerebellar membranes that requires additional attention during equilibration. If this has not been undertaken (no note in the method section), we do recommend carefully investigating the lipid distributions/clustering, including unusual curvature, that might influence the receptors behaviour throughout the simulations, in particular if modulations by ligands are interpreted.

    3. [Methods]: The authors should discuss convergence of the simulated clusters and energy landscape prior to conducting Markov State Modeling.

    4. DRY motif:

      • Please clarify the statements about generalisation in Class F in light of the missing outward kinking. As this proline is present in other Class F receptors, it suggests that this activation feature is likely unique to SMO. Also this molecular switch should be compared to e.g. Rhodopsin, which also signals through Gi (see e.g. Hofmann et al. doi: 10.1016/j.tibs.2009.07.005).

      • The authors should consider additional evidence or be more careful in their statement that the conserved motif (W3.50-G5.39-M6.30) acts as a microswitch in SMO signal transduction. The authors claimed that it is analogous to the DRY motif in class A GPCRs. However, the DRY motif has been shown to be involved in both inactive and active states, validated by experimental data. The R3.50 in DRY motif forms an ionic lock in the inactive state and this ionic interaction is broken during receptor activation. It is unclear what interactions are formed between the W-G-M motif in SMO.

      • Similar to W-G-M, the D-R-E motif has also been claimed as an important site for signal transduction. We recommend caution in this conclusion. The authors mentioned there is an H-bond interaction between D473 and E518. Is there a water molecule between these two residues? The two residues have very low pKa for the carboxylate group and probably are devoid of hydrogens in physiological conditions. Figure 2C should include the R400.

    5. The statement “SAG acts as an agonist by allosterically expanding the tunnel at the cholesterol interaction site” (line 252) may be incorrect according to at least two lines of evidence: 1) elongation of the 4-aminomethyl group of SAG converts it to an antagonist; 2) SMO variants containing mutations at the cholesterol binding site don’t respond to SAG as described in Deshpande et al (Nature 571, 284–288 (2019)). The agonist activity of SAG is most likely due to blocking cholesterol in the 7-TMs. The authors may want to change the statement and conclusion or provide strong evidence to support it.

    Additional suggestions for the authors to consider:

    1. [Fig S.19]: Validity of the MSM on 5 macrostates via the Chapman-Kolmogorov test: the predictions and estimates look identical. Please add a 95% confidence interval and provide scripts used for the calculation and plotting.

    2. Did the authors try to simulate SMO with cholesterol bound to the cysteine rich domain (CRD)? The reorientation of CRD revealed by xSMO crystal structures is controversial in the field because this movement may be a result of crystal packing. It will be very interesting to test whether CRD can undergo this reorientation after cholesterol binding by MD simulation.

    3. [Methods, page 19 line 332]: It is not entirely clear what preparation was done to the SANT1-SMO structure. Please rephrase the sentences to ensure reproducibility

    4. [Methods]: General structure preparation: Where the structures solvated prior insertion into the membrane to avoid collapsing of cavities?

    5. [Methods - MD, page 20]: The authors do not mention the used force-field. Please add.

    6. [Figure S6]: For clarification and comparison (in context of uniqueness), please show the changes in the residues involved in the microswitch for b2AR (6.30, 5.58, 5.66 - also shown for Rhodopsin and others).

    7. [Figure 3]: the authors compared the CRD-TMD junction between inactive and active states. How is the conformation of these residues compared to the determined structures of SMO?

    8. [Results]: It is very interesting that three intermediate states have been determined between inactive and active states. It is unclear how these states (I1., I2, I3) are defined, besides the energy barrier. Are there any signature residues or motifs that can represent each intermediate state?

    9. Cholesterol interactions, distributions and modulations could give valuable insight into their influence on the activation mechanism. As cholesterol is present in the simulations, this data could be easily screened for cholesterol-receptor interactions throughout the activation pathway.

    10. Experimental structures have already revealed the conformational changes between inactive and active SMO, in particular, the shift of TM6 and the movement of W535. This should be clarified in the text and interpreted in light of the new results.

    11. While the authors calculated the mean first passage times during the apo simulation, they did not correlate this to the presence and absence of agonists. This could give further insight into how those modulators are influencing the activation pathway.

    12. [Methods]: The used analysis scripts could be deposited/made available (e.g. how the Chapman-Kolmogorov test was implemented).

    13. The study would have a greater influence on the field by further investigations on the agreement between simulations and experiments.

    REVIEWING TEAM

    Reviewed by:

    Tao Che, Assistant Professor, Washington University in St. Louis, USA: atomic-level understanding of the activation mechanisms of pain-related GPCRs

    Xiaofeng Qi, Postdoctoral Researcher, UT Southwestern Medical Center, USA: structural biology of SMO receptors and Hh/Wnt signaling

    Johanna Tiemann, Postdoctoral Researcher, University of Copenhagen, Denmark: MD simulations of activation mechanisms in Class A GPCRs

    Curated by:

    Alexander S. Hauser, Associate Professor, University of Copenhagen, Denmark

    (This consolidated report is a result of peer review conducted by Biophysics Colab on version 1 of this preprint. Minor corrections and presentational issues have been omitted for brevity.)

  6. Version published to 10.1101/2022.06.07.493647v1 on bioRxiv